Multi-Calibration Boosting R package mcboost version 0.4.4 Implements 'Multi-Calibration Boosting' (2018) <https://proceedings.mlr.press/v80/hebert-johnson18a.html> and 'Multi-Accuracy Boosting' (2019) <doi:10.48550/arXiv.1805.12317> for the multi-calibration of a machine learning model's prediction. 'MCBoost' updates predictions for sub-groups in an iterative fashion in order to mitigate biases like poor calibration or large accuracy differences across subgroups. Multi-Calibration works best in scenarios where the underlying data & labels are unbiased, but resulting models are. This is often the case, e.g. when an algorithm fits a majority population while ignoring or under-fitting minority populations. Software Depends: R (>= 3.1.0) Imports: backports, checkmate (>= 2.0.0), data.table (>= 1.13.6), mlr3 (>= 0.10), mlr3misc (>= 0.8.0), mlr3pipelines (>= 0.3.0), R6 (>= 2.4.1), rmarkdown, rpart, glmnet Suggests: curl, lgr, formattable, tidyverse, PracTools, mlr3learners, mlr3oml, neuralnet, paradox, knitr, ranger, xgboost, covr, testthat (>= 3.1.0) Sebastian Fischer <sebf.fischer@gmail.com> Comprehensive R Archive Network (CRAN) Florian Pfisterer [aut] (ORCID: <https://orcid.org/0000-0001-8867-762X>), Susanne Dandl [ctb] (ORCID: <https://orcid.org/0000-0003-4324-4163>), Christoph Kern [ctb] (ORCID: <https://orcid.org/0000-0001-7363-4299>), Carolin Becker [ctb], Bernd Bischl [ctb] (ORCID: <https://orcid.org/0000-0001-6002-6980>), Sebastian Fischer [ctb, cre] LGPL (>= 3) 2025-07-24 application/tgz https://CRAN.R-project.org/package=mcboost doi:10.32614/CRAN.package.mcboost